Peer to peer (P2P) missing fields and field valuation feedback

ABSTRACT

Methods, systems, and apparatus for processing data, and more specifically to retrieving results for a query from one or more devices coupled to a network. A first device may receive an abstract query including logical fields defined by a first data abstraction model and retrieve query results stored in the first device. The query may be sent to one or more other devices via the network. The one or more other devices may be configured to convert the abstract query to local abstract queries including logical fields defined in local data abstraction models. The local queries may be issued against local databases to retrieve additional results for the query. The local queries may not include all the fields of the abstract query.

BACKGROUND

1. Field

The present invention is generally related to data processing, and more specifically to retrieving data from a database.

2. Description of the Related Art

Databases are computerized information storage and retrieval systems. A relational database management system is a computer database management system (DBMS) that uses relational techniques for storing and retrieving data. The most prevalent type of database is the relational database, a tabular database in which data is defined so that it can be reorganized and accessed in a number of different ways. A distributed database is one that can be dispersed or replicated among different points in a network. An object-oriented programming database is one that is congruent with the data defined in object classes and subclasses.

Regardless of the particular architecture, in a DBMS, a requesting entity (e.g., an application or the operating system) demands access to a specified database by issuing a database access request. Such requests may include, for instance, simple catalog lookup requests or transactions and combinations of transactions that operate to read, change and add specified records in the database. These requests are made using high-level query languages such as the Structured Query Language (SQL) and application programming interfaces (API's) such as Java® Database Connectivity (JDBC). The term “query” denominates a set of commands for retrieving data from a stored database. Queries take the form of a command language, such as SQL, that lets programmers and programs select, insert, update, find the location of data, and so forth.

Any requesting entity, including applications, operating systems and, at the highest level, users, can issue queries against data in a database. Queries may be predefined (i.e., hard coded as part of an application) or may be generated in response to input (e.g., user input). Upon execution of a query against a database, a query result is returned to the requesting entity.

SUMMARY

The present invention is generally related to data processing, and more specifically to retrieving data from a database.

One embodiment of the invention provides a method for retrieving query results. The method generally comprises receiving a first abstract query comprising one or more logical fields defined by a first data abstraction model comprising a plurality of first logical field definitions mapped to physical fields of a first database in a first device, wherein one or more of the first logical fields definitions associate respective first logical fields to respective concepts of a predefined set of concepts, the concepts being standardized metadata. For each of the one or more logical fields of the first abstract query, the method comprises determining whether a second data abstraction model comprises a logical field associated with a concept associated with the respective logical field of the abstract query, wherein the second data abstraction model comprises a plurality of second logical field definitions mapping the second logical fields to physical fields of a second database in a second device, wherein one or more of the second logical fields definitions associate respective second logical fields to respective concepts of the predefined set of concepts. The method further comprises modifying the abstract query to remove the respective logical field from the first abstract query upon determining that the second data abstraction model does not comprise the logical field associated with the concept associated with the respective logical field of the abstract query.

Another embodiment of the invention provides a computer readable storage medium comprising a program product which, when executed, is configured to perform an operation for retrieving query results. The operation generally comprises receiving a first abstract query comprising one or more logical fields defined by a first data abstraction model comprising a plurality of first logical field definitions mapped to physical fields of a first database in a first device, wherein one or more of the first logical fields definitions associate respective first logical fields to respective concepts of a predefined set of concepts, the concepts being standardized metadata. For each of the one or more logical fields of the first abstract query, the operation further comprises determining whether a second data abstraction model comprises a logical field associated with a concept associated with the respective logical field of the abstract query, wherein the second data abstraction model comprises a plurality of second logical field definitions mapping the second logical fields to physical fields of a second database in a second device, wherein one or more of the second logical fields definitions associate respective second logical fields to respective concepts of the predefined set of concepts. The operation also comprises modifying the abstract query to remove the respective logical field from the first abstract query upon determining that the second data abstraction model does not comprise the logical field associated with the concept associated with the respective logical field of the abstract query.

Yet another embodiment of the invention provides a system, comprising a plurality of devices coupled via a network. Each device is generally configured to receive a first abstract query comprising one or more logical fields defined by a first data abstraction model comprising a plurality of first logical field definitions mapped to physical fields of a first database in another device, wherein one or more of the first logical fields definitions associate respective first logical fields to respective concepts of a predefined set of concepts, the concepts being standardized metadata. For each of the one or more logical fields of the first abstract query, each device is configured to determine whether a second data abstraction model comprises a logical field associated with a concept associated with the respective logical field of the abstract query, wherein the second data abstraction model comprises a plurality of second logical field definitions mapping the second logical fields to physical fields of a second database, wherein one or more of the second logical fields definitions associate respective second logical fields to respective concepts of the predefined set of concepts. Each device is also configured to modify the abstract query to remove the respective logical field from the first abstract query upon determining that the second data abstraction model does not comprise the logical field associated with the concept associated with the respective logical field of the abstract query.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features, advantages and objects of the present invention are attained and can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments thereof which are illustrated in the appended drawings.

It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective embodiments.

FIG. 1 illustrates an exemplary system according to an embodiment of the invention.

FIG. 2 illustrates a more detailed view of an exemplary client computer and server, according to an embodiment of the invention.

FIG. 3 illustrates an exemplary relational view 300 of system components according to an embodiment of the invention.

FIG. 4 illustrates a data abstraction model according to an embodiment of the invention.

FIG. 5 illustrates query execution in an exemplary system according to an embodiment of the invention.

FIG. 6 illustrates query execution in another exemplary system according to an embodiment of the invention.

FIG. 7 is a flow diagram of exemplary operations performed by a query manager according to an embodiment of the invention.

FIG. 8 is another flow diagram of exemplary operations performed by a query manager according to an embodiment of the invention.

FIGS. 9 and 10 illustrate exemplary query results, according to an embodiment of the invention.

FIG. 11 illustrates an exemplary logical field definition according to an embodiment of the invention.

FIG. 12 illustrates an exemplary graphical user interface screen according to an embodiment of the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Embodiments of the invention are generally related to data processing, and more specifically to retrieving results for a query from one or more devices coupled to a network. A first device may receive an abstract query including logical fields defined by a first data abstraction model and retrieve query results stored in the first device. The query may be sent to one or more other devices via the network. The one or more other devices may be configured to convert the abstract query to local abstract queries including logical fields defined in local data abstraction models. The local queries may be issued against local databases to retrieve additional results for the query. The local queries may not include all the fields of the abstract query.

In the following, reference is made to embodiments of the invention. However, it should be understood that the invention is not limited to specific described embodiments. Instead, any combination of the following features and elements, whether related to different embodiments or not, is contemplated to implement and practice the invention. Furthermore, although embodiments of the invention may achieve advantages over other possible solutions and/or over the prior art, whether or not a particular advantage is achieved by a given embodiment is not limiting of the invention. Thus, the following aspects, features, embodiments and advantages are merely illustrative and are not considered elements or limitations of the appended claims except where explicitly recited in a claim(s). Likewise, reference to “the invention” shall not be construed as a generalization of any inventive subject matter disclosed herein and shall not be considered to be an element or limitation of the appended claims except where explicitly recited in a claim(s).

As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

Aspects of the present invention are described below with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Exemplary System

FIG. 1 depicts a block diagram of a networked system 100 in which embodiments of the invention may be implemented. In general, the networked system 100 includes at least one client (e.g., user's) computer 101 and a plurality of servers 102 (four such servers 102 a-d shown). The client computer 101 may be coupled with a server 102 (server 102 a in FIG. 1) via a network 140. In general, the network 140 may be a local area network (LAN) and/or a wide area network (WAN). In a particular embodiment, the network 140 is the Internet. In one embodiment, the client computer 101 may be configured to issue queries against the server 102 a and retrieve data from the server 102 a, as will be described in greater detail below.

Each of the servers 102 may be coupled with each other via a network 190. Like network 140, network 190 may also be any one of 140 may be any one or a local area network (LAN), a wide area network (WAN), and/or the Internet. In a particular embodiment of the invention the network 190 may be a peer-to-peer network. A peer-to-peer network is defined herein as any network comprising two or more interconnected devices that are configured to share their respective data, resources, and the like. The devices associated with network 190 may be coupled in any reasonable manner, whether known or unknown, to form any type of P2P network. Exemplary P2P network types include centralized P2P network, decentralized P2P network, structured P2P network, unstructured P2P network, hybrid P2P network, and the like.

Regardless of the type of P2P network 190, generally, any server 102 connected to the P2P network 190 may be configured to independently collect, store, analyze and modify data. Furthermore, the data stored on any server 102 may be transferred to any other server 102 via the network 190. For example, in one embodiment, each server 102 may be configured to issue queries to one or more other servers 102 via the network 190 to retrieve desired data.

While two separate networks 140 and 190 are illustrated in FIG. 1, in alternative embodiments, client computers 101 and the servers 102 may be coupled to the same network, for example, the Internet.

In one embodiment of the invention, in response to receiving a query from the client computer 101, server 102 a may be configured to retrieve query results that are stored therein. The server 102 a may also be configured to transfer the query to one or more other servers 102 via the network 190, retrieve further query results stored in the one or more other server 102, and provide the query results to the client computer 101. Retrieving query results from one or more servers 102 coupled with the P2P network 190 is described in greater detail below.

FIG. 2 illustrates a more detailed view of an exemplary client computer 101 and a server 102, according to an embodiment of the invention. The server 102 may be any one or servers 102 a-d depicted in FIG. 1. The client computer 101 may include a Central Processing Unit (CPU) 211 connected via a bus 220 to a memory 212, storage 216, an input device 217, an output device 218, and a network interface device 219. The input device 217 can be any device to give input to the client computer 101. For example, a keyboard, keypad, light-pen, touch-screen, track-ball, or speech recognition unit, audio/video player, and the like could be used. The output device 218 can be any device to give output to the user, e.g., any conventional display screen. Although shown separately from the input device 217, the output device 218 and input device 217 could be combined. For example, a display screen with an integrated touch-screen, a display with an integrated keyboard, or a speech recognition unit combined with a text speech converter could be used.

The network interface device 219 may be any entry/exit device configured to allow network communications between the client computers 101 and server 102 via the network 140. For example, the network interface device 219 may be a network adapter or other network interface card (NIC).

Storage 216 is preferably a Direct Access Storage Device (DASD). Although it is shown as a single unit, it could be a combination of fixed and/or removable storage devices, such as fixed disc drives, floppy disc drives, tape drives, removable memory cards, or optical storage. The memory 212 and storage 216 could be part of one virtual address space spanning multiple primary and secondary storage devices.

The memory 212 is preferably a random access memory sufficiently large to hold the necessary programming and data structures of the invention. While memory 212 is shown as a single entity, it should be understood that memory 212 may in fact comprise a plurality of modules, and that memory 212 may exist at multiple levels, from high speed registers and caches to lower speed but larger DRAM chips.

Illustratively, the memory 212 contains an operating system 213. Illustrative operating systems, which may be used to advantage, include Linux (Linux is a trademark of Linus Torvalds in the US, other countries, or both) and Microsoft's Windows®. More generally, any operating system supporting the functions disclosed herein may be used.

Memory 212 is also shown containing a query program 114 which, when executed by CPU 211, provides support for issuing queries to server 102. In one embodiment, the query program 214 may include a web-based Graphical User Interface (GUI), which allows the user to display Hyper Text Markup Language (HTML) information. The GUI may be configured to allow a user to create a query, issue the query against a server 102, and display the results of the query. More generally, however, the query program may be a GUI-based program capable of rendering any information transferred between the client computer 101 and the server 102.

The server 102 may be physically arranged in a manner similar to the client computer 101. Accordingly, the server 102 is shown generally comprising a CPU 221, memory 222, and a storage device 226, coupled with one another by a bus 130. Memory 222 may be a random access memory sufficiently large to hold the necessary programming and data structures that are located on server 102.

The server 102 may generally be under the control of an operating system 223 shown residing in memory 222. Examples of the operating system 123 include IBM OS/400®, UNIX, Microsoft Windows®, Linux and the like. More generally, any operating system capable of supporting the functions described herein may be used.

The memory 222 may further include one or more applications 240 and an abstract query interface 246. The applications 240 and the abstract query interface 246 may be software products comprising a plurality of instructions that are resident at various times in various memory and storage devices in the computer system 100. When read and executed by a processor 221 in the server 102, the applications 240 and the abstract query interface 246 cause the computer system 100 to perform the steps necessary to execute steps or elements embodying the various aspects of the invention.

The applications 240 (and more generally, any requesting entity, including the operating system 223) may be configured to issue queries against a database 227 (shown in storage 226). The database 227 is representative of any collection of data regardless of the particular physical representation. By way of illustration, the database 227 may be organized according to a relational schema (accessible by SQL queries) or according to an XML schema (accessible by XML queries). However, the invention is not limited to a particular schema and contemplates extension to schemas presently unknown. As used herein, the term “schema” generically refers to a particular arrangement of data.

In one embodiment, the queries issued by the applications 240 are defined according to an application query specification 242 included with each application 240. The queries issued by the applications 240 may be predefined (i.e., hard coded as part of the applications 240) or may be generated in response to input (e.g., user input). In either case, the queries (referred to herein as “abstract queries”) are composed using logical fields defined by the abstract query interface 246. In particular, the logical fields used in the abstract queries are defined by a data abstraction model 248 of the abstract query interface 246. The abstract queries are executed by a runtime component 250 which transforms the abstract queries into a form consistent with the physical representation of the data contained in the database 227. The application query specification 242 and the abstract query interface 246 are further described with reference to FIG. 3.

The applications 240 may also include a query manager program 244. Query manager 244 may be configured to receive a query from a client computer 101, or an application 240, receive results for the query, and provide the query results to the requesting client computer 101 or application 240. In one embodiment of the invention retrieving query results may involve retrieving query results from the database 227, as described above. In some embodiments, the query manager 244 may be configured to transfer a received query to one or more other servers 102 via the P2P network 190, and retrieve query results from the one or more other servers 102, as will be discussed in greater detail below.

Relational View of Environment

FIG. 3 illustrates an exemplary relational view 300 of components according to an embodiment of the invention. A requesting entity, for example, an application 240 may issue a query 302 as defined by the respective application query specification 242 of the requesting entity. The resulting query 302 is generally referred to herein as an “abstract query” because the query is composed according to abstract (i.e., logical) fields rather than by direct reference to the underlying physical data entities in the database 227. As a result, abstract queries may be defined that are independent of the particular underlying data representation used. In one embodiment, the application query specification 242 may include both criteria used for data selection and an explicit specification of the fields to be returned based on the selection criteria.

The logical fields specified by the application query specification 242 and used to compose the abstract query 302 may be defined by the data abstraction model 248. In general, the data abstraction model 248 may expose information as a set of logical fields that may be used within a query (e.g., the abstract query 302) issued by the application 240 to specify criteria for data selection and specify the form of result data returned from a query operation. The logical fields may be defined independently of the underlying data representation being used in the database 227, thereby allowing queries to be formed that are loosely coupled to the underlying data representation. Abstract queries are described in greater detail in co-pending U.S. patent application Ser. No. 11/226,181, entitled IMPROVED APPLICATION PORTABILITY AND EXTENSIBILITY THROUGH DATABASE SCHEMA AND QUERY ABSTRACTION, filed Sep. 14, 2005, which is incorporated herein by reference in its entirety.

FIG. 4 illustrates an exemplary data abstraction 148 model according to an embodiment of the invention. In general, data abstraction model 148 comprises a plurality of field specifications 408. A field specification may be provided for each logical field available for composition of an abstract query. Each field specification may comprise a logical field name 410 and access method 412. For example, the field specification for Field A in FIG. 3 includes a logical field name 410 a (‘LastName’), and an associated access method 412 a (‘simple’).

The access methods may associate logical field names 410 to a particular physical data representation 314 (See FIG. 3) in a database 227. By way of illustration, two data representations are shown in FIG. 3, an XML data representation 314 ₁, and a relational data representation 314 ₂. However, the physical data representation 314 _(N) indicates that any other data representation, known or unknown, is contemplated. In one embodiment, a single data abstraction model 148 may contain field specifications with associated access methods for two or more physical data representations 314. In an alternative embodiment, a separate data abstraction model 148 may be provided for each separate data representation 314.

Any number of access method types is contemplated depending upon the number of different types of logical fields to be supported. In one embodiment, access methods for simple fields, filtered fields and composed fields are provided. For example, field specifications for Field A exemplify a simple field access method 412 a. Simple fields are mapped directly to a particular entity in the underlying physical data representation (e.g., a field mapped to a given database table and column). By way of illustration, the simple field access method 412 a, shown in FIG. 4 maps the logical field name 410 a (‘LastName’) to a column named “I_name” in a table named “Test Table,” as illustrated.

The field specification for Field X exemplifies a filtered field access method 412 b. Filtered fields identify an associated physical entity and provide rules used to define a particular subset of items within the physical data representation. For example, the filtered field access method 412 b may map the logical field name 410 b to a physical entity in a column named “TestVal” in a table named “Test Table” and may define a filter for the test values. For example, in one embodiment, the filter may define a numerical range in which the test values may be deemed valid.

A composed field access method may also be provided to compute a logical field from one or more physical fields using an expression supplied as part of the access method definition. In this way, information which does not exist in the underlying data representation may be computed. For example, a sales tax field may be composed by multiplying a sales price field by a sales tax rate.

It is contemplated that the formats for any given data type (e.g., dates, decimal numbers, etc.) of the underlying data may vary. Accordingly, in one embodiment, the field specifications 408 may include a type attribute which reflects the format of the underlying data. However, in another embodiment, the data format of the field specifications 408 is different from the associated underlying physical data, in which case an access method is responsible for returning data in the proper format assumed by the requesting entity.

Thus, the access method must know what format of data is assumed (i.e., according to the logical field) as well as the actual format of the underlying physical data. The access method may then convert the underlying physical data into the format of the logical field. By way of example, the field specifications 408 of the data abstraction model 248 shown in FIG. 3 are representative of logical fields mapped to data represented in the relational data representation 314 ₂. However, other instances of the data abstraction model 248 map logical fields to other physical data representations, such as XML.

Each field 408 of the data abstraction model 148 may also include a concept code 409. For example, the concept code for field 408 a may be 101 as illustrated in FIG. 4. Concept code 409 may associate a respective field 408 to a predefined universal concept. For example, field 408 a illustrated in FIG. 4 is associated with a column containing last names. Accordingly, field 408 a is titled “Last Name” and associated with the column “I_name” in the table “Test Table”. However, the concept “last name” may have several synonyms. For example, in some systems last names may be identified as a “surnames” or “family names”. The concept code 409 may provide a means for identifying a universal concept, regardless of how it is specifically labeled in a given system. Accordingly, concept codes may also be referred to herein as “entity resolution attributes” in that these attributes are applied to resolve one local field definition (for a first data abstraction model) to another local field definition (for a second data abstraction model) on the basis of a standardized field definition.

For example, referring to FIG. 1, the data abstraction model in server 102 a may have a logical field named “Last Name” and the data abstraction model in server 102 b may have a logical field named “Family Name”. The concept code for the field “Last Name” in server 102 a and the concept code for the field “Family Name” in server 102 b may both be 101 because they both refer to the same concept.

While a numerical concept code 409 is illustrated in FIG. 4, in alternative embodiments any combination of alphabets, numbers, words, phrases, symbols, and the like may be used to define concept codes. In one embodiment of the invention, the concept code 409 may be derived from a recognized universal vocabulary, such as, for example, a standardized industry-specific vocabulary. Exemplary standardized universal vocabularies may include, among others, UMLS (Universal Medical Language System), MeSH (Medical Subject Headings), SnoMed (Systematic Nomenclature of Medicine), and the like.

Furthermore, while standardized universal vocabularies are described herein with reference to concept codes 409, in alternative embodiments, the concept codes 409 may be generated for internal use by groups of individuals and/or organizations. For example, while working on a project, one or more entities working on the project may agree upon a standardized set on concepts and respective concept codes for categorizing data. Thereafter, each entity may then generate their own respective data abstraction models to store data related to their respective project tasks in their own respective server or system. The data abstraction model generated by each entity may be different. For example, each entity may define its own logical fields in a respective data abstraction model which may be distinct from the logical fields defined by other entities. However, the concept codes used to define fields in the respective data abstraction models may be derived from the agreed upon set of concept codes.

Retrieving Results from Multiple Peer Devices

In one embodiment of the invention, the concept codes may facilitate retrieving query results from a plurality of devices in a P2P network. FIG. 5 illustrates another exemplary system 500 according to an embodiment of the invention. System 500 may be similar to system 100 illustrated in FIG. 1, and therefore may include at least one client computer 101 and a plurality of servers 102, for example, servers 102 a-d coupled to each other via the P2P network 190. As illustrated in FIG. 5, each of the servers 102 a-d may include a respective data abstraction model 248 a-d. The data abstraction models 248 a-d may define logical fields that may be used to compose abstract queries that may be issued against databases in respective servers 102 a-d.

In one embodiment of the invention, the servers 102 a-d may be peer devices operated by entities working on a collaborative project. For example, in a particular embodiment, each of the servers 102 a-d may be associated with a respective university for storing research data. In alternative embodiments, each of the servers 102 a-d may belong to a respective hospital or a department of a hospital, wherein each server 102 stores patient records, medical research data, and the like. More generally, each of the servers 102 a-d may belong to one or more entities, whether individuals or organizations, that collect and store data in an independent and decentralized manner.

A decentralized approach to collecting and storing data may be advantageous because it may allow each entity to collect and store the data without being subject to each others' data collection procedures, data categorizations, analysis and the like. Therefore, the decentralized data collection and storing methods may facilitate a wide variety of entities to be seamlessly integrated into a collaborative project.

However, the independent data collection and storage may also result in difficulties while sharing data between the entities. For example, while performing research on a particular disease, a hospital or university may desire data collected by one or more other hospitals and/or universities to aid the research. However, different categorization of data in each hospital or university server may make it difficult to retrieve such data. For example, as described above, the DAM 248 a may have a logical field named “Last Name” and DAM 248 b may have a logical field named “Family Name”. Furthermore, the DAM 248 c may have a logical field named “Surname”. Therefore, retrieving data related to last names from servers 102 a-c may require separate abstract queries to be written for each of the servers 102 a-c. Manually writing multiple abstract queries and combining the query results may be a tedious, inefficient and error prone process.

In one embodiment of the invention, the fields in the data abstraction models 248 a-d may have similar concepts but may have varying logical field definitions. Embodiments of the invention provide an automated method for retrieving query results from a plurality of servers 102 coupled to a P2P network 109 using concept codes in response to receiving a query. For example, as illustrated in FIG. 5, an abstract query 510 may be sent from a client computer 101 to server 102 a. Alternatively an application program 240 of server 102 a (see FIG. 2) may generate an abstract query 510. The query 510 may be received by the query manager 244 a of the server 102 a. Query manager 244 a may issue the abstract query 510 against a database associated with server 102 a to retrieve at least some of the results of the query.

Furthermore, the query manager 244 a may send the abstract query 510 to one or more of the servers 102 b-d to request further results for the abstract query 510, as illustrated in FIG. 5. For example, in one embodiment, the server 102 a may include a record including a list of the peer computers 102 b-d. Accordingly, the query manager 244 a may be configured to access the record to determine peer computers prior to sending the abstract query 510 to the peer servers 102 b-d. In one embodiment, the query manager 244 a may send the abstract query 510 to all known peers servers. Alternatively, in some embodiment, the query manager 244 a may send the abstract query 510 to a subset of the known peers.

The abstract query 510 may be received by each of query managers 244 b-d at the servers 102 b-d. Each of the query managers 244 b-d may convert the abstract query 510 to a local abstract query based on concept codes as will be described in greater detail below. The query managers 244 b-d may issue the local abstract queries against respective databases associated with the servers 102 b-d to retrieve further results for the abstract query 510.

In one embodiment, the query results from each of the servers 102 b-d may be transferred to the server 102 a via the P2P network 190, as illustrated in FIG. 5. The query results from each of the servers 102 b-d may be received by the query manager 244 a. In one embodiment, the query manager 244 a may combine the results received from the servers 102 b-d with the query results retrieved from the server 102 a and provide the results to a requesting client 101 or application program 240. Alternatively, the query manager may be configured to average and/or normalize the set of results received from the server 102 a-d.

In some embodiments, the abstract query 510 may include one or more clauses that determine how query results are to be presented. For example, in a particular embodiment, the abstract query 510 may include a sort clause that, for example, requires that query results be presented in an ascending or descending order in relation to a particular results field. Accordingly, in some embodiments, the query manager 244 a may be configured to perform one or more operations, for example, sorting, on the combined result set prior to presenting the query results to a requesting entity. In some embodiments, the query manager 244 a may be configured to provide source identification data of the query results to a requesting entity. For example, the query manager 244 a may be configured to identify the particular server 102 a-d from which a particular query result is derived. The identification data may be displayed in an identification field that may be included in the query results.

In one embodiment of the invention, the abstract query 510 received by server 102 from a client 101 or an application program 240 of server 102 a may include logical fields defined by the abstraction model 248 a of server 102 a. An exemplary abstract query 510 is provided below:

-   -   SELECT First Name     -   WHERE Last Name=“Smith”

The abstract query 510 provided above may be configured to retrieve first names of individuals whose last name is “Smith”. Illustratively, the fields “First Name” and “Last Name” may be logical fields defined by the data abstraction model 248 a of server 102 a.

In one embodiment of the invention, abstract query 510 may be transferred to the one or more other servers 102 b-d by query manager 244 a along with concept codes associated with each logical field of the abstract query 510. In one embodiment, the concept codes may be encoded into the abstract query 510. For example, the query manager 244 a may transfer the concept codes for “Last Name” and “First Name” along with the abstract query 510 provided above to the one or more other servers 102 b-d.

Upon receiving the abstract query 510 from server 102 a, each of the one or more query managers 244 b-d may be configured to convert the abstract query 510 to a local abstract query based on the concept codes associated with each logical field of abstract query 510. For example, the DAM 248 b of server 102 b may include the logical fields “Family Name” and “Given Name”. The concept codes associated with the logical fields “Last Name” and “First Name” of DAM 248 a of server 102 a may be the same as the concept codes associated with the logical fields “Family Name” and “Given Name” of DAM 248 b of server 102 b. Accordingly, the query manager 244 b of server 102 b may be configured to generate the following local abstract query upon receiving the abstract query 510 provided above:

-   -   SELECT Given Name     -   WHERE Family Name=“Smith”

Local abstract queries may be similarly generated at each of the servers 102 receiving the abstract query 510 to retrieve results. The results may then be transferred to the server 102 a via the network 190. Upon receiving the query results from the server 102 a and one or more other servers 102 b-d, query program 244 a of server 102 may provide the results to a requesting client computer 101 or application 240.

In one embodiment of the invention, providing the results to a requesting client computer or application may involve performing a union operation to combine results received from each server 102 a-d. However, any other reasonable method of integrating results received from multiple sources, for example, concatenation, may be also used. In alternative embodiments, the results from each source may be provided separately, for example, in separate files, or separated within a given results file. In one embodiment, the results from each of the servers 102 may be displayed in a GUI screen at the client computer 101.

In one embodiment of the invention, the query manager 244 a of server 102 a may be configured to wait until results (or other response) are received from each of the one or more servers 102 b-d before providing the query results to the requesting client computer 101 or application 240. In alternative embodiment, query manager 244 a may wait for a predefined period of time to receive results. If the results are not received from all servers 102 within the predefined period of time, the query program 244 a may be configured to provide only results received within the predefined period of time.

For purposes of illustration only, FIG. 5 shows the query 510 being sent from server 102 a to each of the servers 102 b-d. However, more generally, the query 510 may be sent from any server 102, to any one or more other servers 102 coupled to the P2P network 190. For example, each of the servers 102 of FIG. 5 may be configured to receive abstract queries from respective client computers 101 or application programs 240 and send the query to one or more other servers 102 as described above. Furthermore, in some embodiments the client computer 101 may be directly coupled with the P2P network 190 and configured to issue a query 510 to one or more servers 102. Accordingly, in some embodiments, the client computer 101 may include similar components as the servers 102, for example, a data abstraction model, query manager, and the like.

Furthermore, while embodiments are described herein with respect to a client-server model, this model is merely used for purposes of illustration. Persons skilled in the art will recognize other communication paradigms, all of which are contemplated as embodiments of the present invention. Indeed, as pointed out above, the server computers 102 may in fact be related as peers, rather than computers of in a client-server paradigm. Further, even assuming a client-server model, a given computer may behave as either a client or a server at different times, depending on the context. As such, the terms “client” and “server” are not to be taken as limiting.

FIG. 6 illustrates another system 600 according to an embodiment of the invention. System 600 may include at least one client computer 101 and a plurality of servers 102, as illustrated in FIG. 6. As illustrated, the client computer 101 may be coupled with a server 102 a. The server 102 a may be coupled with a server 102 b via a first P2P network 190, and server 102 b may be coupled to the servers 102 c and 102 d via a second P2P network 191.

As illustrated in FIG. 6, an abstract query 510 may be sent from the client computer 510 to the server 102 a. Server 102 a may send the abstract query to server 102 b via the network 190, as discussed above. The server 102 b may retrieve results for the abstract query 510, for example, by converting the abstract query 510 to a local query, as discussed above. In addition, the query program 244 b of the server 102 b may transfer the query 510 to one or more other peers 102 c and 102 d via the P2P network 191 to retrieve further results for the query 510. For example, server 102 b may include a record including a list of peer servers associated with the server 102 b. Accordingly, query program 244 b may access the record to determine its peer servers, and send the abstract query 510 to one or more of the peers listed in the record.

The server 102 b may receive the results from the servers 102 c and 102 d via network 191, and combine the results with results from the server 102 b before sending the results to the server 102 a via the network 190. In an alternative embodiment, the server 102 b may transfer its own results to the server 102 a via network 190, and then subsequently transfer the results from servers 102 c and 102 d to the server 102 a as they are received.

In some embodiments, each of servers 102 c and 102 d may be coupled with one or more other networks not shown in FIG. 6. Accordingly, the servers 102 c and 102 d may continue to send the query 510 to respective peers via the one or more other networks such that the query 510 cascades through multiple networks and multiple servers 102 to retrieve a comprehensive and complete set of results for the query 510.

The transfer of an abstract query from one server 102 to one or more other servers 102 over a network, for example, networks 190 and 191, is referred to herein as a “network hop”. In one embodiment of the invention, a server 102 or client 101 initiating transfer of an abstract query 510 to one or more other servers 102 may be configured to define a maximum network hops for the abstract query. For example, if the maximum hop for the query is set to 1, the abstract query 510 may only be sent from the server 102 a to the server 102 b via the network 190 (i.e. one network hop), but may not be sent from the server 102 b to the servers 102 c and 102 d.

In one embodiment, the abstract query 510 may include the maximum hop value encoded therein. Furthermore, the abstract query 510 may also include a current number of hops encoded therein. Each server 102 may be configured to update the current hop value encoded in the abstract query 510 before sending the abstract query 510 to one or more other servers 102 via a P2P network. If a server 102 receives an abstract query 510 wherein the maximum hop value is equal to the current hop value, the server 102 may not send the query to any further servers 102.

In some embodiments, a server 102 may be coupled with multiple P2P networks. Therefore, it is possible that the server 102 may receive the same query 510 from each of the multiple P2P networks. However, providing query results each time the abstract query is received may result in a requesting client computer 101 or server 102 receiving duplicate copies of the query results. Therefore, in one embodiment of the invention, the query 510 may include a unique query ID encoded therein. Therefore, if a server 102 receives an abstract query having the same query ID as a previously received abstract query, the server 102 may simply ignore the abstract query or explicitly signal to the sending server that no action will be taken.

FIG. 7 is a flow diagram of exemplary operations performed by a query manager 244 according to an embodiment of the invention. The operations may begin in step 710 by receiving an abstract query. The abstract query may be received from a client computer 101 or an application 240 of a first server 102. Furthermore, the received abstract query may contain logical fields defined according to a first data abstraction model associated with the first server 102.

In step 720 the query manager 244 may issue the abstract query against a database associated with the first server 102 and receive query results. In step 730, the query manager 244 may send the abstract query to one or more second servers 102 via a network. The query manager may then receive results from the abstract query from one or more of the second servers 102 via the network in step 740. In step 750, the query manager 244 may provide the results received from the first server and one or more second servers to the requesting client computer or application 240.

FIG. 8 is a flow diagram of exemplary operations performed by a query manager 244 according to another embodiment of the invention. The operations may begin in step 810 by receiving an abstract query including one or more logical fields defined by a first data abstraction model. In step 820, the query manager 244 may convert the received abstract query to a local abstract query including logical fields defined by a second data abstraction model.

Converting the received abstract query to a local abstract query may involve determining concept codes associated with each of the logical fields associated with the received abstract query. The concept codes may be, in one embodiment, received with the abstract query. The query manager 244 may identify logical fields in the second data abstraction model associated with the concept codes and generate the local abstract query based on the identified logical fields. In step 830, the query manager 244 may issue the local abstract query against a local database to retrieve query results. In step 840, the query manager may provide the query results to a requesting server 102 or client 101.

Retrieving Query Results from Peers with Missing Fields

In some embodiments, it is possible that data abstraction models at one or more servers 102 may not include all the fields of a received query. For example, referring back to FIG. 5, suppose the query 510 includes the following fields:

-   -   SELECT First Name, Age, DOB     -   WHERE Last Name=“Smith” AND city=“Chicago”

The query 510 shown above may return the first name, age, and date of birth for persons whose last name is Smith and live in Chicago. Therefore, the SELECT statement defines the results fields of the query, including First name, Age, and data of birth (DOB). The WHERE statement may define query conditions based on one or more fields, e.g., Last Name and city.

In one embodiment, it is possible that a server 102, e.g., server 102 b may not have one or more fields or concepts defined in the query 510, above. For example, the data abstraction model 248 b may not have fields or concepts associated with Age and city. However, a user may still desire to retrieve data from the server 102 b even though it may not have all the required fields of the query. Accordingly, in one embodiment, the query manager 244 b may be configured to modify a received query by converting the query to a local query as described above, and removing one or more fields from the query and/or disabling one or more query conditions, as is described in greater detail below.

In one embodiment, when a server does not include one or more query results fields, the query manager at the server may simply modify the query by removing the one or more missing query fields. For example, the query manager 244 b may remove the Age field from the SELECT statement prior to executing the query 510 at the server 102 b. By removing the Age field, the query 510 may be successfully executed at the server 102 b without generating any errors.

In one embodiment, after retrieving results for the modified query 510 at the server 102 b, the query manager 244 b may include the missing Age field in the query results, wherein the values in the Age field are shown as NULL values. The query results may then be transferred to a requesting server or client, e.g., the server 102 a illustrated in FIG. 5.

FIG. 9 illustrates an exemplary result set that may be generated by the server 102 b after executing the query 510, according to an embodiment of the invention. As illustrated in FIG. 9, the results 900 may include a plurality of results fields including First Name 910, Age 920, and Date of Birth 930. As illustrated in FIG. 9, the Age results field 920 includes NULL values because the server 102 b may not include an age field or related concept therein.

In one embodiment, the server 102 a may be configured to compile results for the query 510 received from each of the servers 102 b-d, as described hereinabove. Each of the servers 102 b-d may have different results fields missing. FIG. 10 illustrates an exemplary results set 1000 that may be compiled by the server 102 a, according to an embodiment of the invention. As illustrated in FIG. 10, the results set 1000 may include the results fields including First Name 1010, Age 1020, and Date of Birth 1030.

In one embodiment, the records 1051 may represent records retrieved from the server 102 b because the server 102 b may not have any fields or concepts related to the Age field in query 510. The records 1052 may correspond to results retrieved from the server 102 a. The server 102 a may include all the fields of the query 510, accordingly a value if provided for each of the result fields.

In one embodiment, the records 1053 may be received from the server 102 c, which may not have any fields or concepts related to the Date of Birth (DOB) field. Accordingly, the values in the DOB field for records 1053 are shown as NULL values. In one embodiment, the records 1054 may be received from the server 102 d, which may not have any fields or concepts related to the First Name field. Accordingly, the values in the First Name field for records 1054 are shown as NULL values.

As stated above, in one embodiment, the server 102 b may also not include a field or concept related to the city field illustrated in the query 510, above. Because the city field is included in a query condition of query 510, the query manager 244 b may be configured to disable the query condition prior to executing the query at the server 102 b. Disabling the query condition may include, for example, removing one or more operators and one or more conditions from the query. However, in alternative embodiments, any reasonable method for disabling query conditions may be used. Methods for disabling query conditions are described in greater detail in query conditions is described in co-pending U.S. patent application Ser. No. 12/365,996, entitled DISABLING QUERY CONDITIONS IN AN ABSTRACT QUERY ENVIRONMENT, filed on Feb. 5, 2009, which is incorporated herein by reference in its entirety.

In one embodiment, the query manager 244 b may modify the query 510 to the following modified query, in one embodiment:

-   -   SELECT First Name, DOB     -   WHERE Last Name=“Smith”

As illustrated above, the modified query 510 does not include the AND operator and the “city=Chicago” condition based on the city field/concept. Furthermore, the Age field has also been removed because, as stated above, the server 102 b may not include an age field or concept. By removing the missing fields and conditions comprising the missing fields, the query 510 may be executed without error at the server 102 b. Prior to execution of the modified query 510, the query manager 244 b may be configured to convert the query into a local query, as described in the previous sections.

Valuating Fields Based on Received Results

In one embodiment, each of the servers 102 may be configured to dynamically generate a confidence value for each field in its respective data abstraction model. The confidence value may be stored in the field definition of each field, as shown in FIG. 11. FIG. 11 illustrates an exemplary field definition 1100 for a field Y, according to an embodiment of the invention. The field Y may be similar to fields A and X illustrated in FIG. 4, and may be defined by a name, access method, table, column, concept code, and the like. Field Y may also include a confidence value 1111 which may indicate a likelihood of receiving results from peer servers using a P2P query comprising the field.

The value 1111 may be any type of value including a numerical value (e.g., 0 to 10), an alphanumeric value, a descriptive value (e.g., high, medium, and low), or the like. In one embodiment, the value 1111 may be determined based on the results received by a server for peer to peer queries, e.g., the query 510 illustrated in FIG. 5. As described in the previous section, some servers 102 may not include the fields or concepts that exist in data abstraction models of other servers 102. Accordingly, when the servers with missing fields receive queries comprising the missing fields, the query managers at those servers may be configured to remove query conditions having the missing fields and/or provide NULL values in one or more of the result fields, as described above.

In one embodiment, each server 102 may be configured to keep a track of the presence of fields or concepts at other peers 102 based on the received results from the peers. For example, if server 102 b returns results for a query 510 to server 102 a with NULL results for the Age field, as described in the previous section, the query manager 244 a at server 102 a may be configured to determine that the server 102 b does not include the Age field. Based on query results received from multiple peers over time and multiple queries, the query manager 244 a may be configured to determine how many peers include a given field or concept of the data abstraction model 248 a, and how many peers do not.

For example, the query manager 244 a may determine that two peers, servers 102 c and 102 d, have the Age field or concept, whereas one peer, server 102 b, does not. This determination may be made, for example, based on the results 1000 illustrated in FIG. 10. Similar determinations for a number of peers having each field of the data abstraction model 248 a may be made.

The confidence value 1111 in FIG. 11 may be determined by a number of peers also having the field or concept. For example, the greater the number of peers having the field or concept, the greater may be the confidence value. A greater confidence value may indicate a greater likelihood of retrieving data from peer servers 102 by composing a query including the field.

In one embodiment, during query composition, a user may be presented with fields for composing the query, wherein the fields are organized based on the confidence value 1111. FIG. 12 illustrates an exemplary GUI screen 1200 that may be used to compose a query, according to an embodiment of the invention. Specifically, the GUI screen 1200 may be provided to a user while selecting fields for inclusion in a query. As illustrated in FIG. 12, the GUI screen may allow the user to select fields based on their confidence values. For example, a drop down menu 1210 is illustrated to select one of the confidence values High, Medium, Low, and unspecified, as illustrated in FIG. 12.

Based on the selected confidence values, one or more query fields may be displayed for selection in the GUI 1200. For example, because the high confidence value is selected in the drop down menu 1210, the fields Age, Date of Birth, First Name, and Last Name, are displayed for selection using the checkboxes 1220. If a different confidence value, e.g., low, is selected, a different set of query fields may be displayed in the GUI 1200.

While the query fields are organized as high confidence, medium confidence, and low confidence fields in FIG. 12, any reasonable method for arranging query fields based on the confidence value may be implemented in other algorithms. For example, in some embodiments the query fields may be displayed based on ranges of the confidence values. Furthermore, embodiments of the invention are not limited to the graphical tools shown in FIG. 12. In alternative embodiments, any other graphical tools, e.g., buttons, tables, text boxes, and the like may be used to specify the confidence values and display and select the query fields.

Because data abstraction models at each server 102 may constantly change, in one embodiment, query managers in the servers 102 may be configured to dynamically update the confidence values of each query field after receiving results for a query. For example, server 102 a may send a first query to the server 102 b, wherein the query comprises the age field/concept. The server 102 b may respond to the server 102 a with a set of results indicating that the server 102 b does not include the age field. Based on the results of the first query, the query manager 244 a may determine a confidence value for the age field in data abstraction model 248 a.

Thereafter, the server 102 a may send a second query to the server 102 b comprising the age field. The results for the second query may indicate that the server 102 b does include the age field. This may occur, for example, because the data abstraction model 248 b may have been modified to include the age field. Accordingly, upon receiving the results of the second query, the query manager 244 a of server 102 a may update the confidence value of the age field in the data abstraction model 248 a.

While the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow. 

1. A method for retrieving query results, comprising: receiving a first abstract query comprising one or more first logical fields defined by a first data abstraction model comprising a plurality of first logical field definitions mapped to physical fields of a first database in a first device, wherein one or more of the first logical fields definitions associate respective first logical fields to respective concepts of a predefined set of concepts, the concepts being standardized metadata; for each of the one or more first logical fields of the first abstract query, determining whether a second data abstraction model comprises a respective second logical field associated with the respective concept associated with the respective first logical field of the first abstract query, wherein the second data abstraction model comprises a plurality of second logical field definitions mapping the second logical fields to physical fields of a second database in a second device, wherein one or more of the second logical field definitions associate respective second logical fields to respective concepts of the predefined set of concepts; upon determining that the second data abstraction model does not comprise the respective second logical field associated with the respective concept associated with the respective first logical field of the first abstract query, modifying the first abstract query to remove the respective first logical field from the first abstract query; generating a second abstract query based on the first abstract query, wherein the second abstract query comprises one or more of the second logical field definitions of the second data abstraction model; executing the second abstract query to generate results of the modified first abstract query; and modifying the results by including the removed logical field of the first abstract query, and inserting NULL values in the included logical field of the first abstract query.
 2. The method of claim 1, further comprising, for each of the one or more first logical fields of the first abstract query, determining a confidence value of the first logical field based on the modified set of results, wherein the confidence value indicates the availability of the first logical field in a plurality of networked devices.
 3. A non-transitory computer readable storage medium comprising a program product which, when executed, is configured to perform an operation for retrieving query results, comprising: receiving a first abstract query comprising one or more first logical fields defined by a first data abstraction model comprising a plurality of first logical field definitions mapped to physical fields of a first database in a first device, wherein one or more of the first logical fields definitions associate respective first logical fields to respective concepts of a predefined set of concepts, the concepts being standardized metadata; for each of the one or more first logical fields of the first abstract query, determining whether a second data abstraction model comprises a respective second logical field associated with the respective concept associated with the respective first logical field of the first abstract query, wherein the second data abstraction model comprises a plurality of second logical field definitions mapping the second logical fields to physical fields of a second database in a second device, wherein one or more of the second logical field definitions associate respective second logical fields to respective concepts of the predefined set of concepts; upon determining that the second data abstraction model does not comprise the respective second logical field associated with the respective concept associated with the respective first logical field of the first abstract query, modifying the first abstract query to remove the respective first logical field from the first abstract query; generating a second abstract query based on the first abstract query, wherein the second abstract query comprises one or more of the second logical field definitions of the second data abstraction model; executing the second abstract query to generate results of the modified first abstract query; and modifying the set of results by including the removed logical field of the first abstract query, and inserting NULL values in the included logical field of the first abstract query.
 4. The non-transitory computer readable storage medium of claim 3, wherein the operation further comprises, for each of the one or more first logical fields of the first abstract query, determining a confidence value of the first logical field based on the modified set of results.
 5. A system, comprising a plurality of devices coupled via a network, wherein each device is configured to: receive a first abstract query comprising one or more first logical fields defined by a first data abstraction model comprising a plurality of first logical field definitions mapped to physical fields of a first database in a first device, wherein one or more of the first logical fields definitions associate respective first logical fields to respective concepts of a predefined set of concepts, the concepts being standardized metadata; for each of the one or more first logical fields of the first abstract query, determine whether a second data abstraction model comprises a respective second logical field associated with the respective concept associated with the respective first logical field of the first abstract query, wherein the second data abstraction model comprises a plurality of second logical field definitions mapping the second logical fields to physical fields of a second database in a second device, wherein one or more of the second logical field definitions associate respective second logical fields to respective concepts of the predefined set of concepts; upon determining that the second data abstraction model does not comprise the respective second logical field associated with the respective concept associated with the respective first logical field of the first abstract query, modify the first abstract query to remove the respective first logical field from the first abstract query; and modify the set of results by including the removed logical field of the first abstract query, and inserting NULL values in the included logical field of the first abstract query. 